Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries

Capacity estimation is an essential task for battery manage systems to ensure the safety and reliability of lithium-ion batteries. Considering the uncertainty of charging and discharging behavior in practical usage, this paper presents a one-dimensional convolution neural network (1D CNN)-based method that takes random segments of charging curves as inputs to perform capacity estimation for lithium-ion batteries. To improve the robustness and accuracy of the proposed 1D CNN network, a linear decreasing weighted particle swarm optimization algorithm is utilized to optimize the partial hyper parameters of neural network. Experimental data from two sets of batteries with different nominal capacities are employed for verification purpose. It is proved that the proposed method is feasible to provide accurate estimations on capacity degradation for both kinds of batteries. Furthermore, effects of length and relative position of segments on the capacity estimation are also investigated. The analysis results show that a more precise estimation of the battery capacity is prone to be obtained from the segment with a longer length or lower initial SOC. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Capacity estimation is crucial for battery management systems to ensure safety and reliability of lithium-ion batteries. A 1D CNN-based method is proposed in this paper, showing accuracy and feasibility in estimating battery capacity, with investigation on the effects of segment length and relative position on estimation results.